Milestone Achieved in China’s AI Computing Infrastructure

On September 30, a research consortium unveiled a comprehensive open-source toolchain tailored for domestic AI accelerators, comprising three core modules: a high-level language compilation framework, optimized computing libraries, and distributed communication infrastructure. This development establishes China’s first complete bottom-up development capability for AI computing ecosystems, mirroring the technical architecture of established international platforms.

The released component suite contains five specialized modules:

1. DeepGEMM: Matrix operation library supporting FP16/FP32 mixed precision
2. DeepEP: Distributed communication framework enabling cross-device data transfer at 90% PCIe bandwidth utilization
3. TileKernels: Optimization toolkit for vectorized computation and memory access patterns
4. FlashMLA: Acceleration engine for sparse attention mechanisms in transformer models
5. DeepSelect: Data filtering and preprocessing pipeline with dynamic sampling capabilities

These components form a complete infrastructure covering operator development to distributed training. Benchmark tests on super-node architectures show 40% faster training for 100B-parameter models and 65% reduced communication latency compared to conventional implementations.

Technical Architecture Deconstruction

Compilation Layer: Bridging Performance and Productivity

The TileLang compilation framework resolves the traditional dilemma between low-level performance optimization and high-level development efficiency through three-tier abstraction:

# TileLang matrix multiplication example
@tile_kernel
def matmul(A: Tensor[FP16], B: Tensor[FP16]) -> Tensor[FP16]:
    tile_size = get_optimal_tile_size(A.shape, B.shape)  # Automatic tiling strategy
    A_tiled = A.relayout(tile_size)  # Data layout transformation
    B_tiled = B.relayout(tile_size)
    return parallel_reduce(
        lambda i,j: sum(A_tiled[i,k] * B_tiled[k,j] for k in range(tile_size)),
        shape=(A.shape[0], B.shape[1])
    )

Key optimization mechanisms include:

• Hardware-aware tiling: Dynamic block size calculation based on cache hierarchy
• Layout conversion: Automatic handling of NHWC/NCHW format transformations
• Parallel graph generation: Topology-aware computation scheduling

Validation tests show TileLang operators achieve 92% performance of hand-optimized assembly code while improving development productivity by 5x.

Computing Layer: Heterogeneous Architecture Optimization

DeepGEMM implements three critical optimizations for domestic accelerators:

• Precision adaptation: Dynamic FP16/FP32 selection based on computation density
• Fused operations: Combining GEMM, bias addition, and activation into single kernels
• Memory access pattern: Checkerboard data layout minimizing cache conflicts

Benchmark results on domestic accelerators demonstrate 88% of theoretical peak performance for matrix multiplication, with 23% improvement over industry-standard implementations. Dynamic tiling maintains performance stability within 5% variance for irregular matrices.

Communication Layer: Distributed Training Acceleration

DeepEP achieves cross-node communication efficiency through three-layer architecture:

1. Topology-aware routing: Automatic path optimization based on network fabric
2. Computation-communication overlap: Double-buffering mechanism hiding latency
3. Gradient compression: 8:1 data reduction using error-bounded quantization

In 100B-parameter model training scenarios, this framework reduces communication overhead from 35% to 12%, achieving microsecond-level latency through hardware-direct communication channels on super-node architectures.

Developer Ecosystem Enablement

Standardized Development Workflow

The open-source toolkit provides complete development infrastructure including:

• Operator template library: 200+ pre-implemented kernels covering common operations
• Performance profiling tools: Real-time monitoring of compute/communication ratios
• Auto-tuning system: Reinforcement learning-based parameter optimization

Developers follow this standardized pipeline:

graph TD
    A[Requirement Analysis] --> B[TileLang Implementation]
    B --> C{Performance Target?}
    C -->|No| D[Auto-tuning]
    C -->|Yes| E[Integration Testing]
    D --> B
    E --> F[Deployment]

Industry Application Scenarios

Scenario 1: Large-scale Pretraining
For trillion-parameter models, the toolkit enables:

• 3x faster attention computation via FlashMLA
• 30% reduced无效 computation through dynamic data filtering
• 40% lower memory usage with mixed precision training

Scenario 2: Long-context Inference
Addressing challenges in processing sequences exceeding 32K tokens:

• KV cache pooling extending context window to 1M tokens
• 60% computation reduction through structured sparsity
• 2.5x throughput improvement via dynamic batching

Roadmap and Future Directions

The current release focuses on core functionality, with three evolution paths planned:

1. Auto-optimization: Neural architecture search for automatic kernel tuning
2. Heterogeneous scheduling: Unified resource management across CPU/GPU/NPU
3. Security enhancement: Hardware-level encryption and model protection

This toolchain establishes a standardized development paradigm that enables algorithm innovators to focus on model architecture while leveraging bottom-up optimizations. The collaborative model of upper-layer innovation combined with lower-layer optimization represents a critical path for developing mature AI computing ecosystems.

The open-source release marks China’s entry into the full-stack optimization stage of AI computing. As developer communities grow, expected technical advancements include expanded operator libraries, standardized communication protocols, and adaptive compilation strategies, ultimately forming an internationally competitive autonomous AI technology stack.